Precise estimation of integrated quarticity is highly important, while this value provides
inference about integrated volatility and is a valuable ingredient of jump hypothesis
test statistics. Estimation of integrated quarticity based on high frequency
data created additional challenges, which led to development of new measures, robust
to jumps and microstructure noise.
Different combinations of Multipower Volatility Estimators, Nearest Neighbor Truncation
Estimators and Robust Neighborhood Truncation Estimators are analyzed in
detail. After their application to real market data, each of the estimators is assessed
via set of conducted simulation models.
Special attention is paid to the Robust Neighborhood Truncation Estimators which
operate on lower order statistics log-returns, while they were prematurely left out of
analysis in previous works. Performed simulations as well as empirical calculations
proved additional efficiency and jump robustness of these estimators.